CN114280030B - Viscoelastic Characterization Method of Soft Matter Based on Laser-Induced Breakdown Spectroscopy - Google Patents

Abstract

The invention discloses a method for quickly characterizing the viscoelasticity of soft matter based on laser-induced breakdown spectroscopy. After establishing the calibration curve of the viscoelastic parameters of the soft matter and the selected spectral parameters, the invention can perform in-situ on-line rapid detection of the soft matter to be tested. It solves the shortcomings of traditional mechanical rheological measurement methods, such as complex design, time-consuming, and direct contact with the substance to be measured.

Description

Viscoelastic Characterization Method of Soft Matter Based on Laser-Induced Breakdown Spectroscopy

technical field

The invention relates to the field of soft matter testing, in particular to a method for rapidly characterizing the viscoelasticity of soft matter based on laser-induced breakdown spectroscopy.

Background technique

Soft matter refers to a special kind of matter between solid and ideal fluid, which has a wide range of applications in modern human production and life. Soft matter is generally composed of macromolecules or groups, such as polymers, colloids, foams, granular substances, and living system substances that are common in daily life. Viscoelasticity is an important mechanical property of soft matter. The development of convenient and quick soft matter viscoelasticity characterization methods can not only help solve the problems in the preparation and application of soft matter, but also help promote the progress of the basic research field of soft matter.

At present, the conventional means of measuring the viscoelasticity of soft matter is based on mechanical rheological measurement methods, such as various rheometers available on the market, whose working principle is to achieve the characterization of viscoelasticity by measuring the external stress and deformation of the medium. The main disadvantages of conventional mechano-rheological methods are the complex design, time-consuming, and direct contact with the sample to be measured.

Laser-induced breakdown spectroscopy (LIBS), as an emerging material composition analysis technology, has the characteristics of no need for sample pretreatment, multi-element simultaneous analysis, fast real-time and remote online analysis, and has broad application prospects in the fields of industrial process monitoring, geological exploration, food safety testing and environmental pollution monitoring. LIBS analysis technology uses high-energy pulsed laser to ablate the surface of the material to generate plasma, and realizes the quantitative analysis of the chemical composition of the material by measuring the emission spectrum of the plasma. However, the application of laser-induced breakdown spectroscopy in the viscoelastic characterization of soft matter has not been reported.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method for characterizing the viscoelasticity of soft matter based on LIBS technology for the defects of the prior art. This method can quickly characterize the viscoelasticity of soft matter in situ and online, without sampling and sample processing, without direct contact with the substance to be tested, and without any requirement for the internal movement of the substance.

The technical scheme that the present invention takes for solving the problems of the technologies described above is:

The soft matter viscoelasticity characterization method based on LIBS technology provided by the present invention comprises the following steps:

In the first step, prepare n soft material sample groups with different viscoelastic parameters, respectively marked as S ₁ , S ₂ , S ₃ ... S _n , to form a standard sample group;

In the second step, the pulsed laser in the LIBS system is used to irradiate the surface of each soft material sample to generate plasma, and the plasma emission spectrum of the corresponding sample is collected;

The third step is to analyze the collected spectra, and select a certain spectral parameter as the calibration probe corresponding to the viscoelasticity of this group of samples, wherein there is a one-to-one correspondence between the selected spectral parameters and viscoelasticity;

The fourth step is to construct the viscoelastic parameter calibration curve corresponding to this group of soft material samples based on the selected spectral parameters;

The fifth step is to collect the laser plasma emission spectrum corresponding to the soft material to be tested, and obtain the spectral parameters corresponding to the material to be tested;

In the sixth step, compare the spectral parameters corresponding to the substance to be measured with the calibration curve obtained in the fourth step, and then obtain the viscoelastic parameters of the soft substance to be measured.

In the third step of the above method, the spectral parameter is defined as any parameter that can characterize the emission spectrum characteristics of LIBS obtained from the analysis of the collected spectrum, such as the characteristic spectral line intensity or spectral line width corresponding to a certain element in the soft matter, the intensity ratio of two characteristic spectral lines, plasma temperature and electron density, etc.

The above LIBS technology-based soft matter viscoelasticity characterization method specifically includes the following steps:

1) Prepare a set of n standard soft matter samples with different viscoelastic properties, that is, standard samples, and each standard sample is marked as S ₁ , S ₂ , S ₃ ... S _n ;

2) Use LIBS equipment to irradiate the surface of each standard sample in turn to generate plasma, collect and detect the emitted light of the plasma, and finally obtain the corresponding emission spectrum;

3) Select suitable spectral parameters from the corresponding spectrum of each standard sample, wherein the selected standard is that there is a one-to-one correspondence between the spectral parameters and the corresponding viscoelastic parameters of the standard sample;

4) draw the calibration curve with the corresponding viscoelastic parameter value of each standard sample on the horizontal axis, and the selected spectral parameter value corresponding to each standard sample on the vertical axis;

5) based on the parameters of the equipment in step 2), measure the plasma emission spectrum of the soft matter to be tested, and then obtain the spectral parameter values corresponding to step 3);

6) By comparing the spectral parameter values obtained in step 5) with the calibration curve obtained in step 4), the viscoelastic parameter values corresponding to the soft matter to be tested can be quickly and quantitatively characterized.

Above-mentioned method step 1) in, the implication of standard is except that viscoelasticity is different;

The soft matter can be moving, static or quasi-stationary soft matter, and the static or quasi-stationary soft matter refers to the soft matter in which the particles in the surface of the material do not have obvious Brownian motion, such as static particle accumulation materials;

In step 2), the parameter settings of the equipment, including laser parameters, emission spectrum collection and detection parameters, are optimized and kept constant when detecting the substance to be tested;

In step 3), the spectral parameter is defined as any parameter that can characterize the LIBS emission spectral characteristics analyzed from the collected spectrum, such as the characteristic spectral line intensity or spectral line width corresponding to the elements contained in the soft matter, the intensity ratio of two characteristic spectral lines, plasma temperature and electron density, etc.;

Take the intensity ratio of two characteristic spectral lines at different upper energy levels of the same element as an example to illustrate: first extract the integrated intensity of the selected two characteristic spectral lines from the collected spectrum, and then obtain the intensity ratios R ₁ , R ₂ , R ₃ ... R _n of the two spectral lines corresponding to each standard sample. The formula is as follows:

Among them, R _n represents the integral intensity ratio corresponding to the nth standard sample, I ₁ and I ₂ are the emission intensities of the two characteristic spectral lines respectively, C is a constant (the size of C depends on the wavelength corresponding to the selected spectral line, the transition probability of the corresponding energy level and the degree of degeneracy), K _B is the Boltzmann constant, E ₁ and E ₂ represent the energy of the upper energy level of the two characteristic spectral lines respectively, and T _exc is the plasma temperature;

In step 4), the calibration curve constructs a one-to-one correspondence between the viscoelastic parameters and the spectral parameters of this group of soft matter standard samples.

The beneficial effects of the present invention are: the present invention provides a viscoelasticity characterization method of soft matter based on LIBS technology, which solves the shortcomings of traditional mechanical rheological measurement methods such as complex design, time-consuming, and need to directly contact the substance to be measured. After establishing the calibration curve of the viscoelastic parameters of the soft matter and the selected spectral parameters, the invention can quickly detect the soft matter to be tested on-line in situ.

Description of drawings

Fig. 1 is the calibration curve of the porosity (viscoelasticity) of the particulate matter constructed with the intensity ratio of the two characteristic spectral lines as the spectral parameter in Example 1, and the prediction effect on "unknown" samples.

Fig. 2 is the calibration curve of the porosity (viscoelasticity) of the particulate matter constructed with the emission intensity of the characteristic spectral line as the spectral parameter in Example 2, and the prediction effect on the "unknown" sample.

Fig. 3 is the calibration curve of the porosity (viscoelasticity) of the particulate matter constructed with the line width of the characteristic spectral line as the spectral parameter in Example 3, and the prediction effect on the "unknown" sample.

Detailed ways

The present invention will be further described in detail below in conjunction with specific embodiments, and the given examples are only for clarifying the present invention, not for limiting the scope of the present invention. The examples provided below can be used as a guideline for those skilled in the art to make further improvements, and are not intended to limit the present invention in any way.

The experimental methods in the following examples, unless otherwise specified, are conventional methods, carried out according to the techniques or conditions described in the literature in this field or according to the product instructions. The materials and reagents used in the following examples can be obtained from commercial sources unless otherwise specified.

In an embodiment of the present invention, a method for rapidly characterizing the viscoelasticity of soft matter in situ based on LIBS technology is provided. In this method, the emission spectrum characteristics of the plasma generated by laser ablation of particulate matter are used to characterize the size of the viscoelastic parameters of soft matter. The method includes the following steps:

The first step is to prepare n soft material samples with different viscoelastic parameters, respectively marked as S ₁ , S ₂ , S ₃ ... S _n , to form a standard sample group;

In the second step, the pulsed laser in the LIBS system is used to irradiate the surface of each group of soft matter samples to generate plasma, and the plasma emission spectrum of the corresponding sample is collected;

The third step is to analyze the collected spectra, and select a certain spectral parameter as the calibration probe corresponding to the viscoelasticity of this group of samples. There is a one-to-one correspondence between the selected spectral parameters and viscoelasticity. It should be emphasized that the spectral parameters here are defined as any parameter that can characterize the emission spectrum characteristics of LIBS obtained from the analysis of the collected spectra, such as the characteristic spectral line intensity or spectral line width corresponding to a certain element, the intensity ratio of two characteristic spectral lines, plasma temperature or electron density, etc.;

The fourth step is to construct the viscoelastic parameter calibration curve corresponding to this group of soft material samples based on the selected spectral parameters;

The fifth step is to collect the laser plasma emission spectrum corresponding to the soft material to be tested, and obtain the spectral parameters corresponding to the material to be tested;

In the sixth step, compare the spectral parameters corresponding to the substance to be measured with the calibration curve obtained in the fourth step, and then obtain the viscoelastic parameters of the soft substance to be measured.

Specifically include the following steps:

1) Prepare a group of n standard soft matter samples with different viscoelastic properties, that is _, standard samples, and each standard sample is marked as S ₁ , S ₂ , S ₃ .

2) Use LIBS equipment to irradiate the surface of each standard sample in turn to generate plasma, collect and detect the emitted light of the plasma, and finally obtain the corresponding emission spectrum;

The parameter settings of the equipment in step 2), including laser parameters, emission spectrum collection and detection parameters, are all optimized and kept constant when detecting the substance to be tested;

3) Select an appropriate spectral parameter from the corresponding spectrum of each standard sample. The selected standard is the one-to-one correspondence between the spectral parameter and the corresponding viscoelastic parameter of the standard sample; here, the intensity ratio of two characteristic spectral lines at different upper energy levels of the same element is used as an example to illustrate: first extract the integrated intensity of the selected two characteristic spectral lines from the collected spectra, and then obtain the intensity ratios R ₁ , R ₂ , R ₃ ... R _n of the two spectral lines corresponding to each standard sample. The formula is as follows:

Among them, R _n represents the integral intensity ratio corresponding to the nth standard sample, I ₁ and I ₂ are the emission intensities of the two characteristic spectral lines respectively, C is a constant, K _B is the Boltzmann constant, E ₁ and E ₂ represent the upper level energy of the two characteristic spectral lines respectively, and T _exc is the plasma temperature;

4) draw the calibration curve with the corresponding viscoelastic parameter value of each standard sample on the horizontal axis, and the selected spectral parameter value corresponding to each standard sample on the vertical axis;

The calibration curve constructs the one-to-one correspondence between the viscoelastic parameters and the spectral parameters of this group of soft matter standard samples;

5) based on the parameters of the equipment in step 2), measure the plasma emission spectrum of the soft matter to be tested, and then obtain the spectral parameter values corresponding to step 3);

6) By comparing the spectral parameter values obtained in step 5) with the calibration curve obtained in step 4), the viscoelastic parameter values corresponding to the soft matter to be tested can be quickly and quantitatively characterized.

Example 1:

This embodiment is only exemplary, not intended to limit the scope of the present invention and its application. The viscoelastic characterization method of the soft matter of the present invention is described by taking the soft matter formed by stacking copper ball microparticles as an example. This embodiment utilizes the recognized knowledge that there is a one-to-one correspondence between the viscosity coefficient and elastic modulus of the granular material and the porosity of the granular material (see the textbook "Physics and Mechanics of Granular Matter" written by Sun Qicheng et al. in 2011). Therefore, in order to simplify the expression, in this embodiment, the porosity of the particulate matter is used to directly express the viscoelasticity of the particulate matter. This embodiment specifically includes the following steps:

1) The diameter information of the copper ball particles used is d ₅₀ =72 μm, d ₁₀ =59 μm, and d ₉₀ =92 μm. The copper ball particles are naturally filled into 5 sample boxes, the initial filling height is higher than the upper surface of the sample box, and the internal size of the sample box is 70mm (length) × 70mm (width) × 9mm (height). Apply different vibration times to each sample, and then use a scraper to remove the copper particles above the surface of the sample box to make the surface of the sample smooth, and obtain 5 standard soft material samples with different porosity (that is, different viscoelasticity), which are recorded as S ₁ , S ₂ , S ₃ , S ₄ , and S ₅ ;

2) Use a balance to weigh the net mass of each sample as M ₁ =251.52g, M ₂ =246.16g, M 3 =242.10g, M ₄ =238.56g, M ₅ =234.59g; calculate the porosity of each standard sample as P ₁ '=36.3%, P ₂ '=37.7%, P ₃ '=38. 7% _, P ₄ '=39.6%, P ₅ '=40.6%.

3) Place each standard sample on the sample stage of the LIBS equipment in turn, set the moving speed of the sample stage to 8mm/s, set the laser pulse energy to 30mJ, and the frequency to 1Hz, and focus the laser beam on the sample surface through the lens. The sample surface was placed 10mm above the laser beam waist after focusing, the start time of the acquisition time gate of the plasma emission spectrum was 1 μs relative to the laser pulse delay, and the gate width was set to 2 μs. Collect 400 LIBS spectra corresponding to single laser pulses for each group of samples;

4) Divide the 400 spectra corresponding to each sample into eight groups on average (that is, each group contains 50 LIBS spectra corresponding to a single laser pulse); superimpose and then average each group of LIBS spectra; select two characteristic spectral lines CuI:515.3nm and CuI:510.6nm emitted by neutral Cu atoms in the superimposed averaged LIBS spectra to calculate the intensity ratio R and the corresponding standard deviation of these two spectral lines, and obtain the corresponding ratios of each group of samples as R ₁ =1.99 ±, R ₂ =1.95±, R ₃ =1.91±, R ₄ =1.87±, R ₅ =1.75±;

5) Taking the porosity P' corresponding to the sample S _n (n=1,2,4,5) as the abscissa, and the corresponding spectral line intensity ratio R _n as the ordinate, establish a calibration curve for the two as shown in Figure 1. Single exponential fitting is used in the figure, and in different embodiments, multiple regression, univariate fitting, partial least squares method or neural network and other methods can also be used to establish calibration curves.

6) Sample S ₃ is taken as an "unknown" sample, and its corresponding porosity reference value P ₃ ′=38.7% and spectral line intensity ratio R ₃ =1.91± are also plotted in FIG. 1 . It can be seen from the comparison that the prediction result of the porosity of the "unknown" sample using the calibration curve obtained in 5) is consistent with its true value within the error range.

Example 2:

In this embodiment, a calibration curve of porosity is established by using a certain characteristic spectral line intensity as a spectral parameter. On the basis of Example 1, the intensity ratio of the two characteristic spectral lines in step 4) is replaced by the intensity I of a certain characteristic spectral line. Here, the spectral line intensity corresponding to CuI:515.3nm is used as an example for the selected spectral parameters. First, the integrated intensities I ₁ , I ₂ , I ₃ , I ₄ , and I ₅ of the CuI:515.3nm line corresponding to the five groups of samples were extracted respectively; the porosity P' corresponding to the sample S _n (n=1, 2, 4, 5) was taken as the abscissa, and the corresponding spectral line intensity ratio I _n was taken as the ordinate, and a calibration curve of the two was established, as shown in Figure 2. Single exponential fitting is used in the figure, and in different embodiments, multiple regression, univariate fitting, partial least squares method or neural network and other methods can also be used to establish calibration curves. Sample S ₃ is regarded as an "unknown" sample, and its corresponding porosity reference value P ₃ ' and spectral line intensity I ₃ are also plotted in Figure 2. It can be seen from the comparison that the prediction result of the porosity of the "unknown" sample by using the calibration curve is consistent with its true value within the error range.

Example 3:

In this embodiment, the calibration curve of the porosity is established by using the line width of a certain characteristic spectral line as the spectral parameter. On the basis of Example 1, the intensity ratio of the two characteristic spectral lines in step 4) is replaced by the line width W of a certain characteristic spectral line. Here, the spectral line width corresponding to CuI:521.8nm is used as an example to illustrate the selected spectral parameters. First, the full width at half maximum W ₁ , W ₂ , W ₃ , W ₄ , and W ₅ of the line width CuI corresponding to the 521.8nm line of the five groups of samples were respectively extracted; the porosity P' corresponding to the sample S _n (n=1,2,4,5) was taken as the abscissa, and the corresponding spectral line width value W _n was taken as the ordinate, and a calibration curve of the two was established, as shown in Figure 3. Single exponential fitting is used in the figure, and in different embodiments, multiple regression, univariate fitting, partial least squares method or neural network and other methods can also be used to establish calibration curves; sample _S3 is used as an "unknown" sample, and its corresponding porosity reference value _P3 ' and spectral line width _W3 values are also drawn in Figure 3. It can be seen from the comparison that the prediction result of the porosity of the "unknown" sample using the calibration curve is consistent with its true value within the error range.

The spectral parameters shown in the above embodiments can all establish calibration curves for porosity (that is, the viscoelastic parameters of soft matter), and some spectral parameters defined in other ways can also establish a one-to-one correspondence with them, which will not be shown here as examples. It should be noted that any use of the emission spectrum characteristics of plasma generated by LIBS technology to characterize the viscoelasticity of soft matter is included in the scope of this patent right.

The present invention has been described in detail above. For those skilled in the art, without departing from the spirit and scope of the present invention, and without unnecessary experiments, the present invention can be practiced in a wider range under equivalent parameters, concentrations and conditions. While specific embodiments of the invention have been shown, it should be understood that the invention can be further modified. In a word, according to the principles of the present invention, this application intends to include any changes, uses or improvements to the present invention, including changes made by using conventional techniques known in the art and departing from the disclosed scope of this application. Applications of some of the essential features are possible within the scope of the appended claims below.

Claims (8)

1. A method for characterization of soft matter viscoelasticity based on LIBS technology, comprising the steps of: The first step is to prepare n soft material samples with different viscoelastic parameters, respectively marked as S ₁ , S ₂ , S ₃ ... S _n , to form a standard sample group; In the second step, the pulsed laser in the LIBS system is used to irradiate the surface of each soft material sample to generate plasma, and the plasma emission spectrum of the corresponding sample is collected; The third step is to analyze the collected spectra, and select a certain spectral parameter as the calibration probe corresponding to the viscoelasticity of this group of samples, wherein there is a one-to-one correspondence between the selected spectral parameters and viscoelasticity; The fourth step is to construct the viscoelastic parameter calibration curve corresponding to this group of soft material samples based on the selected spectral parameters; The fifth step is to collect the laser plasma emission spectrum corresponding to the soft material to be tested, and obtain the spectral parameters corresponding to the material to be tested; In the sixth step, compare the spectral parameters corresponding to the substance to be measured with the calibration curve obtained in the fourth step, and then obtain the viscoelastic parameters of the soft substance to be measured. 2. The method according to claim 1, characterized in that: in the third step, the spectral parameter is any parameter that can characterize the LIBS emission spectrum characteristic analyzed from the collected spectrum. 3. The method according to claim 2, wherein the spectral parameters are parameters that can characterize spectral features extracted from the collected LIBS emission spectra corresponding to the elements contained in the soft matter. 4. The method according to any one of claims 1-3, characterized in that: the method comprises the following steps: 1) Prepare a set of n standard soft matter samples with different viscoelastic properties, that is, standard samples, and each standard sample is marked as S ₁ , S ₂ , S ₃ ... S _n ; 2) Use LIBS equipment to sequentially irradiate the surface of each standard sample to generate plasma, collect and detect the emitted light of the plasma, and finally obtain the corresponding emission spectrum; 3) Select suitable spectral parameters from the corresponding spectrum of each standard sample, wherein the selected standard is that there is a one-to-one correspondence between the spectral parameters and the corresponding viscoelastic parameters of the standard sample; 4) draw the calibration curve with the corresponding viscoelastic parameter value of each standard sample on the horizontal axis, and the selected spectral parameter value corresponding to each standard sample on the vertical axis; 5) based on the parameters of the equipment in step 2), measure the plasma emission spectrum of the soft matter to be tested, and then obtain the spectral parameter values corresponding to step 3); 6) Comparing the spectral parameter values obtained in step 5) with the calibration curve obtained in step 4), the viscoelastic parameter values of the soft matter to be tested can be quickly and quantitatively characterized. 5. The method according to claim 4, characterized in that: in step 2), the parameter settings of the equipment, including laser parameters, emission spectrum collection and detection parameters, are all optimized and kept constant when detecting the substance to be tested. 6. The method according to claim 4 or 5, characterized in that: in step 3), the spectral parameter is any parameter that can characterize spectral features analyzed from the collected LIBS spectrum. 7. according to the method described in any one in claim 4-6, it is characterized in that: in step 3), take the spectral parameter that selects as the example of two characteristic spectral line intensity ratios of different upper energy levels of the same element: first extract the intensity of selected two characteristic spectral lines from the collected spectrum, then obtain the corresponding two spectral line intensity ratios R ₁ , R ₂ , R ₃ ... R _n of each standard sample, the formula is as follows: Among them, R _n represents the integral intensity ratio corresponding to the nth standard sample, I ₁ and I ₂ are the emission intensities of the two characteristic spectral lines respectively, C is a constant, K _B is the Boltzmann constant, E ₁ and E ₂ represent the upper level energy of the two characteristic spectral lines respectively, and T _exc is the plasma temperature. 8. The method according to any one of claims 4-7, characterized in that: in step 4), the calibration curve constructs a one-to-one correspondence between the viscoelastic parameters and spectral parameters of this group of soft matter standards.